My main interest is in bridging Machine Learning and Neuroscience. I am focusing on building models of the brain using neural networks and deep learning.
Previous work includes research in computer vision, and a little bit of natural language processing and reinforcement learning. My educational background is largely in computer science.
I am currently a PhD/Graduate student at MIT BCS (Brain and Cognitive Sciences) with Jim DiCarlo and collaborate with Josh Tenenbaum and Gabriel Kreiman at Harvard.

The field of Machine Learning is doing pretty well at quantifying its goals and progress, yet Neuroscience is lagging behind in that regard — current claims are often qualitative and not rigorously compared with other models across a wider spectrum of tasks.

Deep neural networks trained on ImageNet classification do the best on our current set of benchmarks and there is a lot of criticism about the mis-alignment between these networks and the primate ventral stream: mapping between the many layers and brain regions is unclear, the models are too large and are just static feed-forward processors.
We thus created a more brain-like model, “CORnet”, which does well on Brain-Score with only four areas and recurrent processing: https://www.biorxiv.org/content/early/2018/09/04/408385

It’s done! I finished my Master’s Thesis which focused on the idea and implementation of recurrent neural networks in computer vision, inspired by findings in neuroscience. The two main applications of this technique shown here are the recognition of partially occluded objects and the integration of context cues.

There is a new project we are beginning to look into which analyzes today’s neural networks in terms of stability and plasticity.
More explicitly, we evaluate how well these networks can cope with changes to their weights and how well they can adapt to new information. Some preliminary results suggest that if weights in lower layers are perturbed, this has a more severe effect on performance than if higher layers are perturbed. This has a nice correlation to neuroscience where it is assumed that our hierarchically lower cortical layers in the visual cortex remain rather fixed over the years.

It is really just a small seminar paper, the main finding is that while using any Machine Learning framework is generally a good idea, TensorFlow has a really good chance of sticking around due to its already widespread usage within Google and research coupled with a growing community.

The work consists of two parts:
Part 1 analyzes the constraints of Hardware Transactional Memory (HTM) and identifies data structures that profit most of this technique.
Part 2 attempts different implementations of HTM in MySQL’s InnoDB storage component and evaluates the results.